Harris eagle optimization algorithm based on random traceless Sigma point variation

Abstract

The invention discloses a Harris eagle optimization algorithm based on random traceless Sigma point variation. The Harris eagle optimization algorithm comprises the following steps: generating initialpositions of N eagles in a feasible region by utilizing a random initialization method; recording the position of the optimal eagle, and carrying out random traceless Sigma point variation; updatingescape energy by using a nonlinear strategy; selecting and executing corresponding exploration and development strategies according to the escape energy and the random numbers, and using quasi-reflection and quasi-reverse solution updating in the development strategies according to conditions; calculating fitness values of eagle population individuals, and determining a current optimal solution and an optimal target value according to an optimal principle; repeating the steps S2-S7 until the maximum number T of iterations is reached; and returning the optimal solution and the optimal target value. The quasi-reverse and quasi-reflection learning strategies enhance the diversity of the population and accelerate the convergence rate of the algorithm; logarithm nonlinear convergence factors are used for balancing algorithm exploration and development capability; and a Sigma point strategy is randomly symmetrical, so that the algorithm is prevented from falling into local optimum.

Description

Technical field [0001] The invention belongs to the technical field of swarm intelligence optimization algorithms, and relates to a Harris Hawk optimization algorithm based on random unscented Sigma point mutation. Background technique [0002] In recent years, with the continuous development and growth of artificial intelligence, urban transportation planning, engineering design, complex networks, and data processing, people are facing more and more complex optimization problems. Using traditional optimization methods to solve these problems is often ineffective or difficult to solve. The emergence of group random optimization algorithms provides a useful supplement to the solution of these problems. The random optimization method of the group is a solution algorithm that people are inspired by the cooperative behavior of the population in biology or nature, and put forward according to its behavior rules, survival criteria and other mechanisms. As a new type of solution metho...

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Problems solved by technology

[0008] The purpose of the present invention is to provide a kind of Harris Eagle optimization algorithm based on random unscented Sigma point mutation, which solves the problem that the HHO algorithm requires the current population to learn towards the current optimal individual when the HHO algorithm is performing population position update iterations; When stuck in a local optimum and unable to jump out, it may cause the algorithm to stagnate or the optimization accuracy is not high

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